Effect of natural antimicrobial agent (MicroGARD) combined with edible coating (NatureSeal) treatment on fresh‐cut butternut squash
Bibliographic record
Abstract
Abstract To improve fresh‐cut produce quality and shelf life, 0.5% or 1.0% MicroGARD ® 730 (MG) as a natural alternative to synthetic chemical preservatives, 2.5% NatureSeal ® (NS) product (vitamin/mineral‐based blends), 0.5% MG combined with 2.5% NS, and 1% MG combined with 2.5% NS were used to treat fresh‐cut butternut squash ( Cucurbita moschata ). The 240 g samples were put into food grade bags and stored at 4 or 7 °C. Microbial population, including aerobic plate counts (APCs), yeast and molds, total coliforms, and quality parameters, including head space O 2 /CO 2 concentration in package, pH, soluble solids, color, and conductivity, were evaluated after 0, 3, 6, 9, 12, and 20 days of storage. Results showed that after 6 days of storage at 7 °C, APC of check and control samples reached to 2.6 × 10 8 and 1.5 × 10 7 CFU/g, respectively; while they were kept at 10 4 CFU/g (3 to 4 log reduction) in the squash samples treated with 0.5% or 1% MG combined with NS at 7 °C. Similar results were found on squash samples stored at 4 °C for 9 days. The cut squash treated with MG combined with NS had APC ≤ 10 7 CFU/g at 4 °C for about 20 days compared to 9 days in controls or 0.5% MG‐treated samples, and 12 days in 1% MG‐treated or NS‐treated samples, respectively. Considering overall quality and extended shelf life, MG combined with NS was recommended to apply to cut squashes stored at 4 °C. Practical Application This research provided useful information and practical treatment application for developing fresh‐cut produce with good quality and extended shelf life up to 20 days at 4 °C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".